![]() ![]() Let's say you do that and have a new flattened dictionary named r. Since CSVs are a widely used, versatile format that can easily be imported into an. You could take the JSON formatted json_data above and unpack it manually, removing nested parts, which means looking through the response and making your own Python dictionary with only single level i.e. While working with APIs, you may need to convert a JSON object to a CSV. ![]() You need to essentially flatten the structure yourself, perhaps decide what is important or what you want to can leave out. The JSON to CSV converter will help you convert your JSON. under "result" there "fields" and then more values, and CSV files can't display that directly. JSON to CSV Converter is easy to use tool to convert JSON to CSV data. There is no simple way to write this directly to a CSV file, because there are nested structures: e.g. The issue is that is will parse it a little strangely. It can read straight from a JSON string (our text above). I would suggest using Pandas, which can do a lot of the tedious work for you very easily. We can use the json module's function loads to load a string): json_data = json.loads(text) The response in this case is a raw string. We get the text data out by using the read() method: text = response.read() First we need to decide which elements we need. Having a valid JSON (or a valid JSON selection) in the active view, from the menu choose Tools -> Command Palette, start typing csv and select the Json2Csv. Lets use the same example json file to transform the stock items into a CSV file. Import json # Used to load data into JSON format Fortunately jq is able to help with this task and make data transformation very simple. It is in Python 2, but I will show you how to make it work in Python 3. On the page you linked there is actually a Python example on how to get the data. ![]()
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